Floating Point Vs. Proportional Control
Provide either two position, floating or proportional modulation control (depending on model selection) of valves in HVAC systems. Features. Two position models controlled by SPST controller. Floating models controlled by SPDT floating controllers. Proportional models controlled by 0-3 Vdc, 6-9 Vdc, 0-10 Vdc, 0-20 mAdc, 2-10 Vdc, or 4. Proportional control, in engineering and process control, is a type of linear feedback control system in which a correction is applied to the controlled variable which is proportional to the difference between the desired value (set point, SP) and the measured value (process value, PV). Two classic mechanical examples are the toilet bowl float proportioning valve and the fly-ball governor. I don't understand really about floating voltages VS grounded voltages if your using differential signals they use Floating voltages but why? When the differential signals go to the op amp they get converted from floating to ground, because inside the IC chip the differential signals get grounded.
The AC500 PM595 Machinery Controller, from ABB, is built around a 1.3 GHz processor with four 32-bit RISC processors plus an embedded double-precision floating point processor, 16 MB of user program memory and a large array of communications interfaces. The controller also has a built-in interface to connect to ABB’s advanced safety PLC for high-risk applications.A motion controller also creates the trajectories that the motors follow in order to meet the desired commands. Also called motion profiles, a profile is a sequence of position commands versus time. This tells the motor where to position the load and how fast it must do so.
The motion controller uses the trajectories it creates to generate the proper torque commands. These torque commands are then sent to the drive which powers the motor.Because of the large amount of signal processing required for these actions, motion controllers typically use digital signal processors (DSPs) for this task. DSPs are specifically designed to perform mathematical operations quickly and efficiently, and can handle the algorithmic processing better than standard microcontrollers, which aren’t designed to handle large amounts of mathematical processing.There are a number of common motion profiles including trapezoidal, ramp, triangular and complex polynomial profiles.
Each is used in certain conditions and situations where that type of motion is desired. For instance, a trapezoidal profile is characterized by constant velocity and acceleration and a graph of the velocity versus time profile is in the shape of a trapezoid.Motion controllers also use some of the basic control laws to implement motion. The simplest of these is called proportional (P) control, which represents a constant integer gain. From P controllers, one can add either a derivative gain (known as D) or an integral gain (or I).
The combination of these three, known as PID, represents one of the most common and powerful types of control algorithm.Practically speaking, motion controllers come in a variety of sizes and types. In general, motion controllers fall into one of three categories; stand-alone, PC-based, and individual microcontrollers.Stand-alone controllers are entire systems typically mounted in one physical enclosure that includes all of the necessary electronics, power supply, and external connections.
These types of controllers can be built into a machine and are dedicated to one motion control application that could involve controlling a single axis of motion or multiple axes.PC-based controllers are mounted onto the motherboard of a basic PC or industrial PC. These types of controllers are mainly processing boards that may generate and execute motion profiles. The advantage of PC-based controllers is that they provide a ready-made graphical user interface that makes programming and tuning the control much easier.Lastly, there are individual microcontrollers.
These are individual ICs that are often designed onto a printed circuit board along with feedback inputs and outputs to drivers to control a motor. While these controllers are relatively inexpensive and have the advantage of giving designers chip-level access to their systems, the drawback is that they may require good programming skills to configure and implement. Servo ControllersA servo controller is the heart of a servo system. A typical servo system consists of a motor, feedback device and the controller.The control circuitry typically involves a motion controller, which generates the motion profile for the motor, and a motor drive which supplies power to the motor based on the commands from the motion controller.
Servo systems are closed-loop systems that have some benefits over open-loop systems including the fact that they improve transient response times, reduce steady state errors and reduce system sensitivity to load parameters.Servo controllers perform two types of tasks; tracking some commanded input and improving a system’s disturbance rejection. One of the most powerful methods of control is PID control.A PID control method works on the error signal which is the difference between a commanded value and the actual value of an output variable, and driving the error to zero. The proportional value can be thought of as a simple gain value.
The integral value integrates the error over a period of time and helps to drive the error to zero. The derivative value helps to stabilize a system that uses an integral and proportional term only. PLCsProgrammable logic controllers (PLCs) are highly specialized, programmable microprocessor-based controllers used to control a specific application on a machine or a process. They are used in automation and manufacturing to control assembly lines and machinery on factory floors as well as many other types of mechanical, electrical, and electronic equipment in a plant.
Bosch Rexroth’s IndraControl XM21/22 PLC combines the speed of the Sercos automation bus with S20 input/output (I/O) series for a scalable control system. For all factory automation and motion logic applications, this controller is easy to configure and features real-time data processing capabilities. It is available with either an Intel 600 or 1,300 MHz ATOM processor, along with an onboard Sercos master featuring a cycle time of 250 µsec.The basic parts of any PLC system include the processor, I/O modules to handle inputs to the controller and outputs to the controlled devices, and some type of user interface which could be as simple as a keypad or a touchscreen interface or a programming link through a PC. The PLC’s processor is programmed through the user interface. The I/O modules are used to bring input signals into the PLC’s CPU and output control signals to controlled devices such as motors, valves, sensors and actuators, among others.One final note: An important consideration for any PLC is the scan time. This is the time in which the PLC runs through the program taking in data and updating outputs. This is typically a few milliseconds but can be much longer depending on the program length and the speed of the processor.
Higher scan times can accommodate processes with more real-time demands than traditional slower applications where scan speed is not as critical. Programmable Automation Controllers (PACs)PACs (programmable automation controllers) are similar to a PLC but denote a controller that accommodates better real-time control needed in some automation applications. Also, special PLCs can include dedicated safety functions that monitor machine inputs such as photoelectric sensors, light curtains, magnetically operated sensors, emergency stop buttons and safety mats.Filed Under.
This two-part article explains five tips to make a fixed-point PI controller work well. I am not going to talk about loop tuning - there are hundreds of articles and books about that; any control-systems course will go over loop tuning enough to help you understand the fundamentals. There will always be some differences for each system you have to control, but the goals are the same: drive the average error to zero, keep the system stable, and maximize performance (keep overshoot and delay low). Save your questions about stability and performance for someone else, and let's focus on implementation details.Let's start with the basics. You have some quantity y(t) you'd like to control to match a command signal u(t).
You can sense y(t) with a sensor. In order to control y(t), you have an actuator that you send a signal x(t).
(Diagram from the. Actuator signal = output of the rightmost summing block = input to plant/process is not labeled, but corresponds to x(t).)The way to do this with a PI controller is to calculate the error e(t) = u(t)-y(t), then calculate x(t) = Kp. e(t) + Ki. integral(e(t)).
(We're not going to include a D term in the controller; most systems don't need one, which is something I'll discuss in Part II. The integral term is important because it lets you reach equilibrium with zero steady-state error; the integrator output is what's able to drive the output with zero error, since at that point the proportional term is zero.)Or at least that's the textbook approach. Canonical discrete-time form: a PI controller in the digital worldAnalog systems can implement the PI controller directly, but digital systems have to restate the problem slightly.In a digital PI controller, you will almost always have a controller that executes at a fixed timestep dt, and instead of continuous-time signals, you have discrete variables that are updated at that fixed timestep.
The usual method, at least conceptually, is to number the timesteps 1, 2, 3, 4. Hi,I am a bit confused by the discrete approximation of the integral action.
It is not the typical backward, forward or trapezoidal, or I am missing something?Here:x(t) = Ki.integral(e(t)) + Kp.e(t)dx/dt = Ki.e(t) + Kp.de/dt(xn-xn-1)/dt = Ki.en + Kp.(en-en-1)/dtFollowing the trapezoidal integration (with forward or backward integration the e(n-1) term will not even appear):int(s) = Ki. e(s)/s1/s = T/2. (z+1)/(z-1)int(z) = Ki.
e(s). T/2. (z+1)/(z-1)int(n) = int(n-1) + Ki. T/2. (e(n) + e(n-1))which adding the proportional term differs in the 1/2 from yours.As usual great and useful stuff!
Thanks for your work:). I also had this problem of understanding the integration equation in the section (2). I've read up, and I found that The derived equation from section (1) is actually the velocity algorithm.The crux of the misunderstanding here is that in section(2), he actually is using the positional algorithm for PID.
Floating Point Vs. Proportional Control Valve
If you derive the canonical form for the positional algorithm for discrete time, you would see that the integral term will stay (in discrete form as well). And staying true to the original equation, the integral term will do a summation OVER THE WHOLE TIME THE SYSTEM IS ON (i.e. From t=0 up to the current time). Thus, we have something like 'eintegral = eintegral + e.dt' as the integrator in that section. And this was quite befuddling for me 'coz I forgot about this from years ago, the positional and velocity algo were not explicitly stated and taught in coursework. For more reading:I think this should be updated to reflect the source of those equations.